Defining an objective measure of the dissimilarity between two images (or parts of them) is a recurrent question in image processing.
When dealing with denoising or deconvolution of images, a dissimilarity measure is needed to evaluate how well the estimate explains the observations. For these problems, efforts have been concentrated in the conditioning of the inverse operator as well as the spatial properties of the estimated images. The measure of fitness to the data is usually a simple Euclidean norm in pixel space such as:
      d    ⁡          (                        l          ⁢                                          ⁢          1                ,                  l          ⁢                                          ⁢          2                    )        =                    ∑                  i          ∈                      {            pixel            }                                                        ⁢                                                                            I                1                            ⁡                              (                ⅈ                )                                      -                                          I                2                            ⁡                              (                ⅈ                )                                                              2            
wherein I1 and I2 are the compared images and d(I1, I2) is the measure of the dissimilarity between the images.
When dealing with tracking or image retrieval, the dissimilarity measure is needed to rank the images of a database according to their visual dissimilarity to a given query image.
In any case, a dissimilarity measure requires to define a feature space i.e. a set of properties that capture the relevant information contained in the image, and to define a dissimilarity measure in this feature space.
The feature space may be based on local or global descriptors. Local descriptors are made of a selected number of points of interest (or salient points) in the image together with a description of their neighborhood. The number of points of interest being limited, much information in the image is not used with these descriptors. The global descriptors such as histograms of intensity values include information of the whole image. The computation of global descriptors may be costly.
The dissimilarity measure can range from simple Euclidean norm to more sophisticated measures: robust estimators have been used for optical flow, Bhattacharya's distance for tracking, entropic measure such as entropy, mutual information for registration.
However, none of the dissimilarity measuring methods proposed until now is satisfactory.
It is desirable to develop a more effective method to measure the dissimilarity between images, as well as a method for ranking images from the most similar to the less similar to a query image, a method for categorizing a query image into at least two categories and a method for measuring the dissimilarity between video sequences.